# NOT RUN {
## load example data and recalculate to Gray
data(ExampleData.DeValues, envir = environment())
ExampleData.DeValues <- ExampleData.DeValues$CA1
## plot the example data straightforward
plot_AbanicoPlot(data = ExampleData.DeValues)
## now with linear z-scale
plot_AbanicoPlot(data = ExampleData.DeValues,
log.z = FALSE)
## now with output of the plot parameters
plot1 <- plot_AbanicoPlot(data = ExampleData.DeValues,
output = TRUE)
str(plot1)
plot1$zlim
## now with adjusted z-scale limits
plot_AbanicoPlot(data = ExampleData.DeValues,
zlim = c(10, 200))
## now with adjusted x-scale limits
plot_AbanicoPlot(data = ExampleData.DeValues,
xlim = c(0, 20))
## now with rug to indicate individual values in KDE part
plot_AbanicoPlot(data = ExampleData.DeValues,
rug = TRUE)
## now with a smaller bandwidth for the KDE plot
plot_AbanicoPlot(data = ExampleData.DeValues,
bw = 0.04)
## now with a histogram instead of the KDE plot
plot_AbanicoPlot(data = ExampleData.DeValues,
hist = TRUE,
kde = FALSE)
## now with a KDE plot and histogram with manual number of bins
plot_AbanicoPlot(data = ExampleData.DeValues,
hist = TRUE,
breaks = 20)
## now with a KDE plot and a dot plot
plot_AbanicoPlot(data = ExampleData.DeValues,
dots = TRUE)
## now with user-defined plot ratio
plot_AbanicoPlot(data = ExampleData.DeValues,
plot.ratio = 0.5)
## now with user-defined central value
plot_AbanicoPlot(data = ExampleData.DeValues,
z.0 = 70)
## now with median as central value
plot_AbanicoPlot(data = ExampleData.DeValues,
z.0 = "median")
## now with the 17-83 percentile range as definition of scatter
plot_AbanicoPlot(data = ExampleData.DeValues,
z.0 = "median",
dispersion = "p17")
## now with user-defined green line for minimum age model
CAM <- calc_CentralDose(ExampleData.DeValues,
plot = FALSE)
plot_AbanicoPlot(data = ExampleData.DeValues,
line = CAM,
line.col = "darkgreen",
line.label = "CAM")
## now create plot with legend, colour, different points and smaller scale
plot_AbanicoPlot(data = ExampleData.DeValues,
legend = "Sample 1",
col = "tomato4",
bar.col = "peachpuff",
pch = "R",
cex = 0.8)
## now without 2-sigma bar, polygon, grid lines and central value line
plot_AbanicoPlot(data = ExampleData.DeValues,
bar.col = FALSE,
polygon.col = FALSE,
grid.col = FALSE,
y.axis = FALSE,
lwd = 0)
## now with direct display of De errors, without 2-sigma bar
plot_AbanicoPlot(data = ExampleData.DeValues,
bar.col = FALSE,
ylab = "",
y.axis = FALSE,
error.bars = TRUE)
## now with user-defined axes labels
plot_AbanicoPlot(data = ExampleData.DeValues,
xlab = c("Data error (%)",
"Data precision"),
ylab = "Scatter",
zlab = "Equivalent dose [Gy]")
## now with minimum, maximum and median value indicated
plot_AbanicoPlot(data = ExampleData.DeValues,
stats = c("min", "max", "median"))
## now with a brief statistical summary as subheader
plot_AbanicoPlot(data = ExampleData.DeValues,
summary = c("n", "in.2s"))
## now with another statistical summary
plot_AbanicoPlot(data = ExampleData.DeValues,
summary = c("mean.weighted", "median"),
summary.pos = "topleft")
## now a plot with two 2-sigma bars for one data set
plot_AbanicoPlot(data = ExampleData.DeValues,
bar = c(30, 100))
## now the data set is split into sub-groups, one is manipulated
data.1 <- ExampleData.DeValues[1:30,]
data.2 <- ExampleData.DeValues[31:62,] * 1.3
## now a common dataset is created from the two subgroups
data.3 <- list(data.1, data.2)
## now the two data sets are plotted in one plot
plot_AbanicoPlot(data = data.3)
## now with some graphical modification
plot_AbanicoPlot(data = data.3,
z.0 = "median",
col = c("steelblue4", "orange4"),
bar.col = c("steelblue3", "orange3"),
polygon.col = c("steelblue1", "orange1"),
pch = c(2, 6),
angle = c(30, 50),
summary = c("n", "in.2s", "median"))
## create Abanico plot with predefined layout definition
plot_AbanicoPlot(data = ExampleData.DeValues,
layout = "journal")
## now with predefined layout definition and further modifications
plot_AbanicoPlot(data = data.3,
z.0 = "median",
layout = "journal",
col = c("steelblue4", "orange4"),
bar.col = adjustcolor(c("steelblue3", "orange3"),
alpha.f = 0.5),
polygon.col = c("steelblue3", "orange3"))
## for further information on layout definitions see documentation
## of function get_Layout()
## now with manually added plot content
## create empty plot with numeric output
AP <- plot_AbanicoPlot(data = ExampleData.DeValues,
pch = NA,
output = TRUE)
## identify data in 2 sigma range
in_2sigma <- AP$data[[1]]$data.in.2s
## restore function-internal plot parameters
par(AP$par)
## add points inside 2-sigma range
points(x = AP$data[[1]]$precision[in_2sigma],
y = AP$data[[1]]$std.estimate.plot[in_2sigma],
pch = 16)
## add points outside 2-sigma range
points(x = AP$data[[1]]$precision[!in_2sigma],
y = AP$data[[1]]$std.estimate.plot[!in_2sigma],
pch = 1)
# }
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